24 papers · ranked by Valyu relevance
Boris Knyazev, Carolyn Augusta, Graham W. Taylor, Chi Ho Yeung
Reasoning about graphs evolving over time is a challenging concept in many domains, such as bioinformatics, physics, and social networks. We consider a common case in which edges can be short term interactions (e.g., messaging) or long term structural connections (e.g., friendship). In practice, long term edges are…
Leshanshui Yang, Clément Chatelain, Sébastien Adam
In recent years, Dynamic Graph (DG) representations have been increasingly used for modeling dynamic systems due to their ability to integrate both topological and temporal information in a compact representation. Dynamic graphs allow to efficiently handle applications such as social network prediction, recommender…
Peng Mei, Yu hong Zhao
Dynamic network link prediction is extensively applicable in various scenarios, and it has progressively emerged as a focal point in data mining research. The comprehensive and accurate extraction of node information, as well as a deeper understanding of the temporal evolution pattern, are particularly crucial in the…
Most Tahmina Rahman, Mohammad Al Olaimat, Serdar Bozdag
Electronic Health Records (EHRs) contain vast amounts of longitudinal patient medical history data, making them highly informative for early disease prediction. Numerous computational methods have been developed to leverage EHR data; however, many process multiple patient records simultaneously, resulting in high…
Sanaz Hasanzadeh Fard
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Shaohan Wei, Marco Piangerelli
Graph embedding has gained significant popularity due to its ability to represent large-scale graph data by mapping nodes to a low-dimensional space. However, most of the existing research in this field has focused on transductive learning, where fixed node embeddings are generated by training the entire graph. This…
Falih Gozi Febrinanto, Feng Xia, Kristen Moore, Chandra Thapa + 1 more
'Charų C. Aggarwal'] Abstract—Graph learning is a popular approach for performing machine learning on graph-structured data. It has revolutionized the machine learning ability to model graph data to address downstream tasks. Its application is wide due to the availability of graph data ranging from all types of…
Yusen Zhang
Dynamic graph learning is crucial for modeling real-world systems with evolving relationships and temporal dynamics. However, the lack of a unified benchmark framework in current research has led to inaccurate evaluations of dynamic graph models. This paper highlights the significance of dynamic graph learning and its…
Sophie Loman, Lorenzo Caciagli, Shubhankar P. Patankar, Ari E. Kahn + 3 more
Humans naturally attend to patterns that emerge in our perceptual environments, building mental models that allow future experiences to be processed more effectively and efficiently. Perceptual events and statistical relations can be represented as nodes and edges in a graph. Recent work in graph learning has shown…
Yanping Zheng, Lu Yi, Zhewei Wei
Graph neural networks (GNNs) have emerged as a powerful tool for effectively mining and learning from graph-structured data, with applications spanning numerous domains. However, most research focuses on static graphs, neglecting the dynamic nature of real-world networks where topologies and attributes evolve over…
Yicong Huang, Zhuliang Yu, Luis Javier García Villalba
Latent variable models (LVMs) for neural population spikes have revealed informative low-dimensional dynamics about the neural data and have become powerful tools for analyzing and interpreting neural activity. However, these approaches are unable to determine the neurophysiological meaning of the inferred latent…
Ilya Makarov, Andrey Savchenko, Arseny Korovko, Leonid Sherstyuk + 5 more
'Nikita Severin' 'Dmitrii Kiselev' 'Aleksandr Mikheev' 'Dmitrii Babaev' 'Yilun Shang'] Many tasks in graph machine learning, such as link prediction and node classification, are typically solved using representation learning. Each node or edge in the network is encoded via an embedding. Though there exists a lot of…
Tingting Chen, Hongming Li, Hao Zheng, Yong Fan
Characterizing brain dynamic functional connectivity (dFC) patterns from functional Magnetic Resonance Imaging (fMRI) data is of paramount importance in imaging neuroscience and medicine. Recently, many graph neural network (GNN) models, combined with transformers or recurrent neural networks (RNNs), have shown great…
Yanping Zheng, Hanzhi Wang, Zhewei Wei, Jiajun Liu + 1 more
Graph Neural Networks (GNNs) have been widely used for modeling graph-structured data. With the development of numerous GNN variants, recent years have witnessed groundbreaking results in improving the scalability of GNNs to work on static graphs with millions of nodes. However, how to instantly represent continuous…
Hui Wei, Fushun Li
The brain, an exceedingly intricate information processing system, poses a constant challenge to memory research, particularly in comprehending how it encodes, stores, and retrieves information. Cognitive psychology studies memory mechanism from behavioral experiment level and fMRI level, and neurobiology studies…
Mohamed Radwan, Pedro G. Lind, Anis Yazidi
In this study, we investigate the use of temporal dynamics in brain connectivity for the classification of electroencephalography (EEG) signals using dynamic Graph Neural Networks (GNNs). Our methods are applied to several large-scale EEG datasets focused on abnormality and epilepsy detection. The implemented models…
Charley M. Wu, Eric Schulz, Samuel J. Gershman
From social networks to public transportation, graph structures are a ubiquitous feature of life. Yet little is known about how humans learn functions on graphs, where relationships are defined by the connectivity structure. We adapt a Bayesian framework for function learning to graph structures, and propose that…
Chaoyue Sun, Ruogu Fang, Marco Salemi, Mattia Prosperi + 1 more
In the midst of an outbreak or sustained epidemic, reliable prediction of transmission risks and patterns of spread is critical to inform public health programs. Projections of growth or decline among specific risk groups can aid in optimizing interventions, particularly when resources are limited. Phylogenetic trees…
David Buterez, Jon Paul Janet, Steven Kiddle, Pietro Liò
We investigate the potential of graph neural networks for transfer learning and improving molecular property prediction on sparse and expensive to acquire high-fidelity data by leveraging low-fidelity measurements as an inexpensive proxy for a targeted property ofinterest. This problem arises in discovery processes…
Zachary J. Gale-Day, Laura Shub, Kangway V. Chuang, Michael J. Keiser
Message-passing neural networks (MPNNs) on molecular graphs generate continuous and differentiable encodings of small molecules with state-of-the-art performance on protein-ligand complex scoring tasks. Here, we describe the Protein-Graph Network (PGN) package, an open-source toolkit that constructs ligand-receptor…
Maria Boulougouri, Pierre Vandergheynst, Daniel Probst
Computational representation of molecules can take many forms, including graphs, stringencodings of graphs, binary vectors, or learned embeddings in the form of real-valued vectors. These representations are then used in downstream classification and regression tasks using a wide range of machine-learning models.…
Authors not listed
Real-world datasets in chemical engineering and bioengineering processes--such as those from catalytic reactors, multiphase flows, polymerization reactors, bioreactors, and clinical trials--can often be unlabelled or disorganized, rendering the training of existing supervised learning models ineffective at learning the…
Authors not listed
Inverse molecular design aims to generate novel chemical structures that satisfy multiple property constraints, yet reinforcement-learning (RL) fine-tuning can be sensitive to how objectives are converted into a scalar reward. Here, we systematically analyze how scalarization choices and stabilization mechanisms shape…
Authors not listed
Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…